A monocular wide-field vision system for geolocation with uncertainties in urban scenes

In engineering applications related to video surveillance, the use of monocular omnidirectional cameras would reduce costs and complications associated with infrastructure, installation, synchronization, maintenance and operation of multiple cameras. This makes omnidirectional cameras very useful for transport analysis, a key task of which is to accurately geolocate vehicles and/or pedestrians observed in an ample region. The problem of measuring on the plane was previously solved for monocular central perspective images. However, the problem of determining uncertainties in geolocalization using monocular omnidirectional images, has not been addressed. This problem is not trivial due to the complexity of the image formation models associated with these cameras. The contributions of this work are: (1) The geolocation problem is solved using omnidirectional monocular images through a Bayesian inference approach. (2) The calculation of Bayesian marginalization integrals is simplified through first-order approximations. (3) The accuracy of the estimated positions and uncertainties is shown through Monte Carlo simulations under realistic measurement conditions. (4) The method to geolocate a vehicle's trajectory on a satellite map is applied in an urban setting.